The Role of Artificial Intelligence in Hardware Engineering and Manufacturing

It took me eight months to build my first FPV drone. I procured the parts in January, received them in February, remembered I bought them in June, assembled and lit the drone on fire in July, re-assembled and crashed it into my neighbor’s roof in August, and then, and only then, at the behest of my wife, downloaded a FPV flight simulator. A super-intelligent AI surely could have done better. While far from superhuman today, the frontier models can now generate CAD, design custom PCBs, source COTS parts, and describe how those parts are to be assembled. And they’re only getting better. Hardware design benchmarks like CAD Arena, EE Bench, and my own LuxoBench show steady improvements; and more generally, you’d have to be crazy to bet against the Bitter Lesson and say hardware design will forever stay beyond the frontier. Superhuman hardware engineers are coming soon, perhaps very soon. But what will they look like? Will superhuman hardware engineers surpass all humans in one fell swoop, or only in specific domains/disciplines? And what will hardware engineering look like before, during, and after the crossover? My answer to each of these questions starts with one simple observation: that the interface between the digital and physical world is lossy. This makes it hard to “write” changes from the digital world of bits into the physical world of atoms, and hard to “read” measurements of the physical world back into a computer. This lossiness will shape when and how future superhuman hardware engineers will emerge — and means that even once the models can consistently one-shot electromechanical designs, they will still have a lot more work to do. ### Write access: from digital designs to assembled atoms When OpenAI released GPT-6 Astra, the online world erupted with demonstrations of its 3D design prowess. These demonstrations got hardware engineers wondering if the models could also generate engineering-grade CAD. We quickly found that — unfortunately — the models by themselves are still not quite ready for primetime hardware engineering. There are many levels to building real-world hardware, and clearing each one requires different, and often entirely independent, skills. At the 101 level, you have to decompose the product down into components, identify available commercial-off-the-shelf (“COTS”) parts, ensure their mutual compatibility, design the mechanical and electrical interconnections between them, ensure the final design is buildable, and then actually procure parts from online stores. At the 201 level, however, things get even harder: you now have to make custom parts, which means navigating the complex translation step from digital design tools to physical machines. At the 301 level, you take those simple parts and start to bring them together into a complex electromechanical assembly. At the 401 level, you realize that your design needs not only COTS parts and custom-designed parts, but also either inputs or particularly complex assemblies from other external vendors. And then, finally, at the 501 level, you have to do all of the above at massive scale. Each of these “writing” problems, from the 101 to 501 level, are all fundamentally solvable but need to be solved individually. ### Read access: observing and measuring the real world One of my friends buys 45 million chickens per week. But despite his efforts to buy the same spec from the same suppliers and run everything through the same process with the same machines, his week-to-week variance in saleable food products after processing has historically been as high as 20%. It is exceptionally hard to know precisely what is going on in the real, physical world. First, the real world is non-uniform, and constantly changing. Second, real-world data is sparse. Third, real-world data is often expensive to collect. Fourth, the real world has inherent latency. And, fifth, some information about the physical world is qualitative — and gathering such data is often the most incompressible step of real-world hardware design. ### Hardware development in an AGI world Hardware engineering is iterative: you design, build, test, and iterate, and each loop through this process takes time. The design step is digital, and therefore very model-legible. But designing hardware is only the very beginning of the hardware engineering process. Once you design the part, you still have to build, test, and iterate — and each subsequent step requires more physical-world interaction than the one before. First, hardware design needs better simulation tools. Second, we expect demand to “shift left,” i.e., move closer to raw materials. Third, the AI labs need to acquire “process knowledge.” I suspect that the most incompressible step of the design process — meaning, the step that even a truly superhuman hardware engineer would struggle to expedite — is gathering feedback from the end users of hardware products. Superhuman hardware engineers are coming fast. I expect the frontier models to quickly solve hardware design, which will shift the frontier to the remaining steps of hardware engineering (DFM, testing, iteration) that require greater interaction with the real, physical world. Advancing that frontier will mean solving a whole new set of challenges: improving the interface between the digital and physical worlds; finding ways to more quickly discover customer preferences; and building data pipelines that can approximate the process knowledge currently only held by master technicians.